{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "MELANOMA_Tumor_Size_Prediction.ipynb",
      "provenance": []
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    }
  },
  "cells": [
    {
      "cell_type": "code",
      "metadata": {
        "id": "5N3q4e4MFg8d",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "import pandas as pd\n",
        "import numpy as np\n",
        "import seaborn as sns\n",
        "import matplotlib.pyplot as plt\n",
        "from sklearn.model_selection import cross_val_score"
      ],
      "execution_count": 7,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "5SbWeZgyFoq6",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "train=pd.read_csv(\"/content/Train.csv\")\n",
        "test=pd.read_csv(\"/content/Test.csv\")\n",
        "target=train.pop(\"tumor_size\")\n",
        "sub=pd.read_csv(\"/content/sample_submission.csv\")\n",
        "data=pd.concat([train,test],ignore_index=True)"
      ],
      "execution_count": 8,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "VuhnjR1dLUR1",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 185
        },
        "outputId": "34ab1403-daef-4a88-e3a3-3eeb4d1f33a5"
      },
      "source": [
        "data.isnull().sum() , data.shape"
      ],
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(mass_npea         0\n",
              " size_npear        0\n",
              " malign_ratio      0\n",
              " damage_size       0\n",
              " exposed_area      0\n",
              " std_dev_malign    0\n",
              " err_malign        0\n",
              " malign_penalty    0\n",
              " damage_ratio      0\n",
              " dtype: int64, (45730, 9))"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 9
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "SYC7KAWHLUjx",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 195
        },
        "outputId": "a7333461-c36e-4d63-ee7f-8cc252ad989e"
      },
      "source": [
        "data.head(5)"
      ],
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
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              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>mass_npea</th>\n",
              "      <th>size_npear</th>\n",
              "      <th>malign_ratio</th>\n",
              "      <th>damage_size</th>\n",
              "      <th>exposed_area</th>\n",
              "      <th>std_dev_malign</th>\n",
              "      <th>err_malign</th>\n",
              "      <th>malign_penalty</th>\n",
              "      <th>damage_ratio</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>6930.90</td>\n",
              "      <td>2919.02</td>\n",
              "      <td>0.42116</td>\n",
              "      <td>51.8298</td>\n",
              "      <td>9.888294e+05</td>\n",
              "      <td>109.487</td>\n",
              "      <td>2758.76</td>\n",
              "      <td>72</td>\n",
              "      <td>39.3620</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>15635.70</td>\n",
              "      <td>4879.36</td>\n",
              "      <td>0.31206</td>\n",
              "      <td>223.5500</td>\n",
              "      <td>2.058426e+06</td>\n",
              "      <td>248.881</td>\n",
              "      <td>5952.53</td>\n",
              "      <td>240</td>\n",
              "      <td>22.0253</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>10376.20</td>\n",
              "      <td>2613.88</td>\n",
              "      <td>0.25191</td>\n",
              "      <td>127.3370</td>\n",
              "      <td>1.434676e+06</td>\n",
              "      <td>160.093</td>\n",
              "      <td>4635.26</td>\n",
              "      <td>73</td>\n",
              "      <td>29.9963</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>13093.80</td>\n",
              "      <td>4510.06</td>\n",
              "      <td>0.34444</td>\n",
              "      <td>155.4400</td>\n",
              "      <td>1.812195e+06</td>\n",
              "      <td>173.015</td>\n",
              "      <td>5273.87</td>\n",
              "      <td>32</td>\n",
              "      <td>28.1354</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>7545.21</td>\n",
              "      <td>2882.36</td>\n",
              "      <td>0.38201</td>\n",
              "      <td>85.1237</td>\n",
              "      <td>1.043918e+06</td>\n",
              "      <td>124.414</td>\n",
              "      <td>3263.35</td>\n",
              "      <td>57</td>\n",
              "      <td>35.0200</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   mass_npea  size_npear  ...  malign_penalty  damage_ratio\n",
              "0    6930.90     2919.02  ...              72       39.3620\n",
              "1   15635.70     4879.36  ...             240       22.0253\n",
              "2   10376.20     2613.88  ...              73       29.9963\n",
              "3   13093.80     4510.06  ...              32       28.1354\n",
              "4    7545.21     2882.36  ...              57       35.0200\n",
              "\n",
              "[5 rows x 9 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 10
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "RrRAesBTLl9M",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 185
        },
        "outputId": "3d630b18-2be3-4619-efb3-cafa59b4f542"
      },
      "source": [
        "data.dtypes"
      ],
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "mass_npea         float64\n",
              "size_npear        float64\n",
              "malign_ratio      float64\n",
              "damage_size       float64\n",
              "exposed_area      float64\n",
              "std_dev_malign    float64\n",
              "err_malign        float64\n",
              "malign_penalty      int64\n",
              "damage_ratio      float64\n",
              "dtype: object"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 11
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "y-eesDBXLUhh",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 284
        },
        "outputId": "98a3149c-6b9a-48a4-d02b-4dbd5dd507ac"
      },
      "source": [
        "data.describe()"
      ],
      "execution_count": 12,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
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              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>mass_npea</th>\n",
              "      <th>size_npear</th>\n",
              "      <th>malign_ratio</th>\n",
              "      <th>damage_size</th>\n",
              "      <th>exposed_area</th>\n",
              "      <th>std_dev_malign</th>\n",
              "      <th>err_malign</th>\n",
              "      <th>malign_penalty</th>\n",
              "      <th>damage_ratio</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>count</th>\n",
              "      <td>45730.000000</td>\n",
              "      <td>45730.000000</td>\n",
              "      <td>45730.000000</td>\n",
              "      <td>45730.000000</td>\n",
              "      <td>4.573000e+04</td>\n",
              "      <td>45730.000000</td>\n",
              "      <td>45730.000000</td>\n",
              "      <td>45730.000000</td>\n",
              "      <td>45730.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>9871.596995</td>\n",
              "      <td>3017.367175</td>\n",
              "      <td>0.302392</td>\n",
              "      <td>103.492433</td>\n",
              "      <td>1.368299e+06</td>\n",
              "      <td>145.638061</td>\n",
              "      <td>3989.755990</td>\n",
              "      <td>69.975071</td>\n",
              "      <td>34.523664</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>4058.138034</td>\n",
              "      <td>1464.324663</td>\n",
              "      <td>0.062886</td>\n",
              "      <td>55.424985</td>\n",
              "      <td>5.640367e+05</td>\n",
              "      <td>69.999230</td>\n",
              "      <td>1993.574575</td>\n",
              "      <td>56.493443</td>\n",
              "      <td>5.979755</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>2392.050000</td>\n",
              "      <td>403.500000</td>\n",
              "      <td>0.092500</td>\n",
              "      <td>10.310100</td>\n",
              "      <td>3.194902e+05</td>\n",
              "      <td>31.970400</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>15.228000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>6936.680000</td>\n",
              "      <td>1979.045000</td>\n",
              "      <td>0.258740</td>\n",
              "      <td>63.563900</td>\n",
              "      <td>9.535912e+05</td>\n",
              "      <td>94.757500</td>\n",
              "      <td>3165.322500</td>\n",
              "      <td>31.000000</td>\n",
              "      <td>30.424725</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>8898.805000</td>\n",
              "      <td>2668.155000</td>\n",
              "      <td>0.300150</td>\n",
              "      <td>87.740800</td>\n",
              "      <td>1.237219e+06</td>\n",
              "      <td>126.176000</td>\n",
              "      <td>3840.170000</td>\n",
              "      <td>54.000000</td>\n",
              "      <td>35.299300</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>12126.150000</td>\n",
              "      <td>3786.410000</td>\n",
              "      <td>0.342890</td>\n",
              "      <td>133.646750</td>\n",
              "      <td>1.690920e+06</td>\n",
              "      <td>181.468500</td>\n",
              "      <td>4644.192500</td>\n",
              "      <td>91.000000</td>\n",
              "      <td>38.870800</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>max</th>\n",
              "      <td>40034.900000</td>\n",
              "      <td>15312.000000</td>\n",
              "      <td>0.577690</td>\n",
              "      <td>369.317000</td>\n",
              "      <td>5.472011e+06</td>\n",
              "      <td>598.408000</td>\n",
              "      <td>105948.170000</td>\n",
              "      <td>350.000000</td>\n",
              "      <td>55.300900</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "          mass_npea    size_npear  ...  malign_penalty  damage_ratio\n",
              "count  45730.000000  45730.000000  ...    45730.000000  45730.000000\n",
              "mean    9871.596995   3017.367175  ...       69.975071     34.523664\n",
              "std     4058.138034   1464.324663  ...       56.493443      5.979755\n",
              "min     2392.050000    403.500000  ...        0.000000     15.228000\n",
              "25%     6936.680000   1979.045000  ...       31.000000     30.424725\n",
              "50%     8898.805000   2668.155000  ...       54.000000     35.299300\n",
              "75%    12126.150000   3786.410000  ...       91.000000     38.870800\n",
              "max    40034.900000  15312.000000  ...      350.000000     55.300900\n",
              "\n",
              "[8 rows x 9 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 12
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "NWWV-QGgLUfL",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 314
        },
        "outputId": "d59c1014-2570-41c8-afee-94ef44d4eb32"
      },
      "source": [
        "data.corr()"
      ],
      "execution_count": 13,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
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              "\n",
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              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>mass_npea</th>\n",
              "      <th>size_npear</th>\n",
              "      <th>malign_ratio</th>\n",
              "      <th>damage_size</th>\n",
              "      <th>exposed_area</th>\n",
              "      <th>std_dev_malign</th>\n",
              "      <th>err_malign</th>\n",
              "      <th>malign_penalty</th>\n",
              "      <th>damage_ratio</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>mass_npea</th>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.906606</td>\n",
              "      <td>0.126285</td>\n",
              "      <td>0.931056</td>\n",
              "      <td>0.998185</td>\n",
              "      <td>0.967531</td>\n",
              "      <td>0.554002</td>\n",
              "      <td>0.651251</td>\n",
              "      <td>-0.898240</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>size_npear</th>\n",
              "      <td>0.906606</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.502562</td>\n",
              "      <td>0.793143</td>\n",
              "      <td>0.902853</td>\n",
              "      <td>0.908354</td>\n",
              "      <td>0.515929</td>\n",
              "      <td>0.584242</td>\n",
              "      <td>-0.786188</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>malign_ratio</th>\n",
              "      <td>0.126285</td>\n",
              "      <td>0.502562</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.031230</td>\n",
              "      <td>0.122622</td>\n",
              "      <td>0.200650</td>\n",
              "      <td>0.080064</td>\n",
              "      <td>0.095340</td>\n",
              "      <td>-0.069039</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>damage_size</th>\n",
              "      <td>0.931056</td>\n",
              "      <td>0.793143</td>\n",
              "      <td>0.031230</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.925707</td>\n",
              "      <td>0.938108</td>\n",
              "      <td>0.485185</td>\n",
              "      <td>0.676919</td>\n",
              "      <td>-0.891766</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>exposed_area</th>\n",
              "      <td>0.998185</td>\n",
              "      <td>0.902853</td>\n",
              "      <td>0.122622</td>\n",
              "      <td>0.925707</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.961769</td>\n",
              "      <td>0.553729</td>\n",
              "      <td>0.642961</td>\n",
              "      <td>-0.897765</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>std_dev_malign</th>\n",
              "      <td>0.967531</td>\n",
              "      <td>0.908354</td>\n",
              "      <td>0.200650</td>\n",
              "      <td>0.938108</td>\n",
              "      <td>0.961769</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.538198</td>\n",
              "      <td>0.662584</td>\n",
              "      <td>-0.881973</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>err_malign</th>\n",
              "      <td>0.554002</td>\n",
              "      <td>0.515929</td>\n",
              "      <td>0.080064</td>\n",
              "      <td>0.485185</td>\n",
              "      <td>0.553729</td>\n",
              "      <td>0.538198</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.347030</td>\n",
              "      <td>-0.521148</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>malign_penalty</th>\n",
              "      <td>0.651251</td>\n",
              "      <td>0.584242</td>\n",
              "      <td>0.095340</td>\n",
              "      <td>0.676919</td>\n",
              "      <td>0.642961</td>\n",
              "      <td>0.662584</td>\n",
              "      <td>0.347030</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>-0.637287</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>damage_ratio</th>\n",
              "      <td>-0.898240</td>\n",
              "      <td>-0.786188</td>\n",
              "      <td>-0.069039</td>\n",
              "      <td>-0.891766</td>\n",
              "      <td>-0.897765</td>\n",
              "      <td>-0.881973</td>\n",
              "      <td>-0.521148</td>\n",
              "      <td>-0.637287</td>\n",
              "      <td>1.000000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                mass_npea  size_npear  ...  malign_penalty  damage_ratio\n",
              "mass_npea        1.000000    0.906606  ...        0.651251     -0.898240\n",
              "size_npear       0.906606    1.000000  ...        0.584242     -0.786188\n",
              "malign_ratio     0.126285    0.502562  ...        0.095340     -0.069039\n",
              "damage_size      0.931056    0.793143  ...        0.676919     -0.891766\n",
              "exposed_area     0.998185    0.902853  ...        0.642961     -0.897765\n",
              "std_dev_malign   0.967531    0.908354  ...        0.662584     -0.881973\n",
              "err_malign       0.554002    0.515929  ...        0.347030     -0.521148\n",
              "malign_penalty   0.651251    0.584242  ...        1.000000     -0.637287\n",
              "damage_ratio    -0.898240   -0.786188  ...       -0.637287      1.000000\n",
              "\n",
              "[9 rows x 9 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 13
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "uFMlQ54uLzO7",
        "colab_type": "text"
      },
      "source": [
        "## EDA"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "O5hkLksRLpMy",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "def nor(x):\n",
        "  sk=x.skew()\n",
        "  sq=(np.sqrt(x)).skew()\n",
        "  lg=(np.log1p(x)).skew()\n",
        "  return {\"skew\":sk,\"sqrt\":sq,\"log\":lg}"
      ],
      "execution_count": 14,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "YUxxvdIuLpKO",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 841
        },
        "outputId": "9c1f1441-17d5-4566-bcb6-bec4e5247a87"
      },
      "source": [
        "plt.figure(figsize=(17,14))\n",
        "plt.subplot(3,3,1)\n",
        "sns.distplot(data.mass_npea)\n",
        "plt.subplot(3,3,2)\n",
        "sns.distplot(data.size_npear)\n",
        "plt.subplot(3,3,3)\n",
        "sns.distplot(data.malign_ratio)\n",
        "plt.subplot(3,3,4)\n",
        "sns.distplot(data.damage_size)\n",
        "plt.subplot(3,3,5)\n",
        "sns.distplot(data.exposed_area)\n",
        "plt.subplot(3,3,6)\n",
        "sns.distplot(data.std_dev_malign)\n",
        "plt.subplot(3,3,7)\n",
        "sns.distplot(data.err_malign)\n",
        "plt.subplot(3,3,8)\n",
        "sns.distplot(data.malign_penalty)\n",
        "plt.subplot(3,3,9)\n",
        "sns.distplot(data.damage_ratio)"
      ],
      "execution_count": 15,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7fc91f45d9b0>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 15
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 1224x1008 with 9 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "FBnFWDkIO8rt",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 84
        },
        "outputId": "65345157-7173-4dbc-93a5-9bee408ca085"
      },
      "source": [
        "data.columns"
      ],
      "execution_count": 16,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Index(['mass_npea', 'size_npear', 'malign_ratio', 'damage_size',\n",
              "       'exposed_area', 'std_dev_malign', 'err_malign', 'malign_penalty',\n",
              "       'damage_ratio'],\n",
              "      dtype='object')"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 16
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "M7tmsl0_L3q7",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 722
        },
        "outputId": "51b34a2c-42ce-46db-cfc4-296e99d6273a"
      },
      "source": [
        "from IPython.display import display\n",
        "display(print(\"1. mass_npea\"))\n",
        "display(nor(data.mass_npea))\n",
        "display(print(\"2. size_npear\"))\n",
        "display(nor(data.size_npear))\n",
        "display(print(\"3. malign_ratio\"))\n",
        "nor(data.malign_ratio)\n",
        "display(print(\"4. damage_size\"))\n",
        "display(nor(data.damage_size))\n",
        "display(print(\"5. exposed_area\"))\n",
        "display(nor(data.exposed_area))\n",
        "display(print(\"6. std_dev_malign\"))\n",
        "display(nor(data.std_dev_malign))\n",
        "display(print(\"7. err_malign\"))\n",
        "display(nor(data.err_malign))\n",
        "display(print(\"8. malign_penalty\"))\n",
        "display(nor(data.malign_penalty))\n",
        "display(print(\"9. damage_ratio\"))\n",
        "display(nor(data.damage_ratio))"
      ],
      "execution_count": 17,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "1. mass_npea\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "None"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "{'log': 0.13277783894849488,\n",
              " 'skew': 1.0922013818038485,\n",
              " 'sqrt': 0.5990078684955066}"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "2. size_npear\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "None"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "{'log': -0.07659414382230043,\n",
              " 'skew': 1.1906519653249468,\n",
              " 'sqrt': 0.5420932327600916}"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "3. malign_ratio\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "None"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "4. damage_size\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "None"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "{'log': 0.15951235087970422,\n",
              " 'skew': 1.2251031380799065,\n",
              " 'sqrt': 0.6857948051877383}"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "5. exposed_area\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "None"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "{'log': 0.10731821340193755,\n",
              " 'skew': 1.0587669899068137,\n",
              " 'sqrt': 0.5704827230828677}"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "6. std_dev_malign\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "None"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "{'log': 0.11882170779416629,\n",
              " 'skew': 1.120759900387105,\n",
              " 'sqrt': 0.6146481212041276}"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "7. err_malign\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "None"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "{'log': -0.21246048244341043,\n",
              " 'skew': 20.76264434293341,\n",
              " 'sqrt': 3.88922744635358}"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "8. malign_penalty\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "None"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "{'log': -0.5021134141747344,\n",
              " 'skew': 1.682933176719256,\n",
              " 'sqrt': 0.6690790196434717}"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "9. damage_ratio\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "None"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "{'log': -0.9176452841856719,\n",
              " 'skew': -0.4726457982840867,\n",
              " 'sqrt': -0.6942170704353294}"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ujHG1iQOFv-k",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "t=pd.concat([train,test],ignore_index=True)"
      ],
      "execution_count": 18,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "4WIsuesOMRIl",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 77
        },
        "outputId": "51406e29-6b4c-4166-fdc5-e73eb6e1118c"
      },
      "source": [
        "t.head(1)"
      ],
      "execution_count": 19,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>mass_npea</th>\n",
              "      <th>size_npear</th>\n",
              "      <th>malign_ratio</th>\n",
              "      <th>damage_size</th>\n",
              "      <th>exposed_area</th>\n",
              "      <th>std_dev_malign</th>\n",
              "      <th>err_malign</th>\n",
              "      <th>malign_penalty</th>\n",
              "      <th>damage_ratio</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>6930.9</td>\n",
              "      <td>2919.02</td>\n",
              "      <td>0.42116</td>\n",
              "      <td>51.8298</td>\n",
              "      <td>988829.407</td>\n",
              "      <td>109.487</td>\n",
              "      <td>2758.76</td>\n",
              "      <td>72</td>\n",
              "      <td>39.362</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   mass_npea  size_npear  ...  malign_penalty  damage_ratio\n",
              "0     6930.9     2919.02  ...              72        39.362\n",
              "\n",
              "[1 rows x 9 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 19
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "XI4ePOSoGqxJ",
        "colab_type": "text"
      },
      "source": [
        "### TRANSFORMATION"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "kxOjcy2TF4Y1",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "t.mass_npea=np.sqrt(t.mass_npea)"
      ],
      "execution_count": 20,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "i93lDfz3GtQS",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "t.size_npear=np.log1p(t.size_npear)\n",
        "size=np.log1p(t.size_npear)"
      ],
      "execution_count": 21,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "tgi9r3hAGtOR",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "t.malign_ratio=np.log1p(t.malign_ratio)"
      ],
      "execution_count": 22,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "JzKuUs0LJTRB",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "t.damage_size=np.sqrt(t.damage_size)"
      ],
      "execution_count": 23,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "kW2wcqkMJTPL",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "t.exposed_area=np.sqrt(t.exposed_area)"
      ],
      "execution_count": 24,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "gl7hF2fEKs5J",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "t.std_dev_malign=np.log1p(t.std_dev_malign)"
      ],
      "execution_count": 25,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "FlEDD2MSKs2g",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "t.malign_penalty=np.sqrt(t.malign_penalty)"
      ],
      "execution_count": 26,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "TsBl2hE5BAEK",
        "colab_type": "text"
      },
      "source": [
        "## Dropping features with high corr"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Xqqr-QyL_TvJ",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "t.drop(\"size_npear\",axis=1,inplace=True)"
      ],
      "execution_count": 27,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "lp2Bk0MHNqzd",
        "colab_type": "text"
      },
      "source": [
        "## FEATURE ENGINEERING"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "yAe7O-oNORp0",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "t[\"p/e\"]=t.malign_penalty/t.err_malign"
      ],
      "execution_count": 28,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "zPC7ySzJDchy",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "t[\"mass/exposed_area\"]=(t.mass_npea/t.exposed_area)"
      ],
      "execution_count": 29,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "_yB0loW0NDYO",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "t[\"mass/std\"]=t.mass_npea/t.std_dev_malign"
      ],
      "execution_count": 30,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "LJ4b3DvrNts4",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "t[\"error/std\"]=t.err_malign/t.std_dev_malign"
      ],
      "execution_count": 31,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "zVXmA57C5oqH",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "t[\"m/a*s\"]=t[\"mass/exposed_area\"]*t.damage_size"
      ],
      "execution_count": 32,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "hWouvGzsCdIS",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "t[\"m/a_sqrt\"]=np.sqrt(t[\"mass/exposed_area\"])"
      ],
      "execution_count": 33,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "t5iirl64CeIW",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "t[\"error-penalty\"]=t.damage_ratio-t.malign_ratio"
      ],
      "execution_count": 34,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "9Vqr28zMBwy4",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "t[\"FE1\"]=t[\"mass/exposed_area\"]*t[\"p/e\"]"
      ],
      "execution_count": 35,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "V9FgQxjw9COz",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "t[\"FE2\"]=t[\"mass/exposed_area\"]*t[\"mass/std\"]"
      ],
      "execution_count": 36,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "a1ixmW-0yvNn",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "t[\"FE3\"]=t.malign_ratio+t.std_dev_malign"
      ],
      "execution_count": 37,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "2O9U6GbQzfnA",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "t[\"FE4\"]=t.damage_ratio-t.std_dev_malign"
      ],
      "execution_count": 38,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "tzN2ao_b5_4o",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "t[\"FE5\"]=t[\"m/a*s\"]-t.malign_ratio"
      ],
      "execution_count": 39,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Iknh4uJ2NqKc",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 173
        },
        "outputId": "c4c62d66-869d-4c7b-9e52-7d02c7adfd74"
      },
      "source": [
        "t.head(3)"
      ],
      "execution_count": 40,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>mass_npea</th>\n",
              "      <th>malign_ratio</th>\n",
              "      <th>damage_size</th>\n",
              "      <th>exposed_area</th>\n",
              "      <th>std_dev_malign</th>\n",
              "      <th>err_malign</th>\n",
              "      <th>malign_penalty</th>\n",
              "      <th>damage_ratio</th>\n",
              "      <th>p/e</th>\n",
              "      <th>mass/exposed_area</th>\n",
              "      <th>mass/std</th>\n",
              "      <th>error/std</th>\n",
              "      <th>m/a*s</th>\n",
              "      <th>m/a_sqrt</th>\n",
              "      <th>error-penalty</th>\n",
              "      <th>FE1</th>\n",
              "      <th>FE2</th>\n",
              "      <th>FE3</th>\n",
              "      <th>FE4</th>\n",
              "      <th>FE5</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>83.252027</td>\n",
              "      <td>0.351473</td>\n",
              "      <td>7.199292</td>\n",
              "      <td>994.399018</td>\n",
              "      <td>4.704898</td>\n",
              "      <td>2758.76</td>\n",
              "      <td>8.485281</td>\n",
              "      <td>39.3620</td>\n",
              "      <td>0.003076</td>\n",
              "      <td>0.083721</td>\n",
              "      <td>17.694758</td>\n",
              "      <td>586.359168</td>\n",
              "      <td>0.602732</td>\n",
              "      <td>0.289346</td>\n",
              "      <td>39.010527</td>\n",
              "      <td>0.000258</td>\n",
              "      <td>1.481422</td>\n",
              "      <td>5.056371</td>\n",
              "      <td>34.657102</td>\n",
              "      <td>0.251258</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>125.042793</td>\n",
              "      <td>0.271598</td>\n",
              "      <td>14.951589</td>\n",
              "      <td>1434.721681</td>\n",
              "      <td>5.520985</td>\n",
              "      <td>5952.53</td>\n",
              "      <td>15.491933</td>\n",
              "      <td>22.0253</td>\n",
              "      <td>0.002603</td>\n",
              "      <td>0.087155</td>\n",
              "      <td>22.648639</td>\n",
              "      <td>1078.164532</td>\n",
              "      <td>1.303102</td>\n",
              "      <td>0.295220</td>\n",
              "      <td>21.753702</td>\n",
              "      <td>0.000227</td>\n",
              "      <td>1.973936</td>\n",
              "      <td>5.792583</td>\n",
              "      <td>16.504315</td>\n",
              "      <td>1.031503</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>101.863634</td>\n",
              "      <td>0.224670</td>\n",
              "      <td>11.284370</td>\n",
              "      <td>1197.779654</td>\n",
              "      <td>5.081982</td>\n",
              "      <td>4635.26</td>\n",
              "      <td>8.544004</td>\n",
              "      <td>29.9963</td>\n",
              "      <td>0.001843</td>\n",
              "      <td>0.085044</td>\n",
              "      <td>20.044077</td>\n",
              "      <td>912.096924</td>\n",
              "      <td>0.959665</td>\n",
              "      <td>0.291623</td>\n",
              "      <td>29.771630</td>\n",
              "      <td>0.000157</td>\n",
              "      <td>1.704623</td>\n",
              "      <td>5.306652</td>\n",
              "      <td>24.914318</td>\n",
              "      <td>0.734994</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "    mass_npea  malign_ratio  damage_size  ...       FE3        FE4       FE5\n",
              "0   83.252027      0.351473     7.199292  ...  5.056371  34.657102  0.251258\n",
              "1  125.042793      0.271598    14.951589  ...  5.792583  16.504315  1.031503\n",
              "2  101.863634      0.224670    11.284370  ...  5.306652  24.914318  0.734994\n",
              "\n",
              "[3 rows x 20 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 40
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "kPx_67eLL8V2",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "5facfe81-5870-45c3-abc0-c0509832ca81"
      },
      "source": [
        "t.shape, train.shape , test.shape , target.shape"
      ],
      "execution_count": 41,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "((45730, 20), (9146, 9), (36584, 9), (9146,))"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 41
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "4mFyuyA8L8Tb",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "#data splitting\n",
        "trn=t[0:9146]\n",
        "tst=t[9146:]"
      ],
      "execution_count": 42,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "2V-_adcHJ65r",
        "colab_type": "text"
      },
      "source": [
        "### ENSEMBLING"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "X4KAo2gtiG17",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "from sklearn.ensemble import StackingRegressor\n",
        "from sklearn.ensemble import ExtraTreesRegressor\n",
        "from sklearn.ensemble import BaggingRegressor\n",
        "from lightgbm import LGBMRegressor\n",
        "\n",
        "bag=BaggingRegressor(n_estimators=150,max_features=13)\n",
        "lgb=LGBMRegressor(n_estimators=900,num_leaves=48,reg_lambda=50,reg_alpha=20)\n",
        "etc=ExtraTreesRegressor(n_estimators=400,max_depth=26,min_samples_split=2,max_features=10)"
      ],
      "execution_count": 44,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "1TA1mirVPeXs",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 114
        },
        "outputId": "fa7f2fe5-4518-4f35-cccd-f025499c106f"
      },
      "source": [
        "tst.loc[tst[\"err_malign\"] == 0.0]"
      ],
      "execution_count": 45,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>mass_npea</th>\n",
              "      <th>malign_ratio</th>\n",
              "      <th>damage_size</th>\n",
              "      <th>exposed_area</th>\n",
              "      <th>std_dev_malign</th>\n",
              "      <th>err_malign</th>\n",
              "      <th>malign_penalty</th>\n",
              "      <th>damage_ratio</th>\n",
              "      <th>p/e</th>\n",
              "      <th>mass/exposed_area</th>\n",
              "      <th>mass/std</th>\n",
              "      <th>error/std</th>\n",
              "      <th>m/a*s</th>\n",
              "      <th>m/a_sqrt</th>\n",
              "      <th>error-penalty</th>\n",
              "      <th>FE1</th>\n",
              "      <th>FE2</th>\n",
              "      <th>FE3</th>\n",
              "      <th>FE4</th>\n",
              "      <th>FE5</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>9526</th>\n",
              "      <td>129.032554</td>\n",
              "      <td>0.315446</td>\n",
              "      <td>13.467516</td>\n",
              "      <td>1499.067596</td>\n",
              "      <td>5.555596</td>\n",
              "      <td>0.0</td>\n",
              "      <td>11.532563</td>\n",
              "      <td>25.3804</td>\n",
              "      <td>inf</td>\n",
              "      <td>0.086075</td>\n",
              "      <td>23.225692</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.159219</td>\n",
              "      <td>0.293386</td>\n",
              "      <td>25.064954</td>\n",
              "      <td>inf</td>\n",
              "      <td>1.999156</td>\n",
              "      <td>5.871041</td>\n",
              "      <td>19.824804</td>\n",
              "      <td>0.843774</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "       mass_npea  malign_ratio  damage_size  ...       FE3        FE4       FE5\n",
              "9526  129.032554      0.315446    13.467516  ...  5.871041  19.824804  0.843774\n",
              "\n",
              "[1 rows x 20 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 45
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "nnTNAJMGQUiE",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 101
        },
        "outputId": "2d99f8f7-4136-4fe6-fabf-74e6079ec6f0"
      },
      "source": [
        "tst[\"p/e\"][9526]=-1\n",
        "tst[\"FE1\"][9526]=-1"
      ],
      "execution_count": 46,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/IPython/core/interactiveshell.py:2882: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  exec(code_obj, self.user_global_ns, self.user_ns)\n"
          ],
          "name": "stderr"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ABzpC1IRiGzk",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "estimators=[(\"bag\",bag),(\"lgb\",lgb),(\"etc\",etc)]"
      ],
      "execution_count": 47,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "wS365e9fiGwz",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "reg = StackingRegressor(estimators=estimators)"
      ],
      "execution_count": 48,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "rR7L5N5pi3m0",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "ffb8f34c-42f5-412c-9825-252da480dda5"
      },
      "source": [
        "scores=cross_val_score(reg, trn,target, cv = 5, scoring = \"neg_root_mean_squared_error\")\n",
        "scores.mean()"
      ],
      "execution_count": 49,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "-3.5837112705526524"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 49
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "VlyQ1t8kGYaZ",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "reg.fit(trn,target)\n",
        "pre=reg.predict(tst)\n",
        "sub[\"tumor_size\"]=pre\n",
        "sub.to_csv(\"sub_02.csv\",index=False)"
      ],
      "execution_count": null,
      "outputs": []
    }
  ]
}